{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dpath = \"../data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#相似度计算\n",
    "def sim_cal(s1, s2):\n",
    "    similarity = 0.0\n",
    "    sum1=np.sum(s1)  \n",
    "    sum2=np.sum(s2)  \n",
    "    sum1Sq=np.sum(s1**2)  \n",
    "    sum2Sq=np.sum(s2**2)  \n",
    "    pSum=np.sum(s1*s2)  \n",
    "        \n",
    "    #分子\n",
    "    num=pSum-(sum1*sum2/n)  \n",
    "        \n",
    "    #分母\n",
    "    den=np.sqrt((sum1Sq-sum1**2/n)*(sum2Sq-sum2**2/n))  \n",
    "    if den==0:  \n",
    "        similarity = 0\n",
    "    else: \n",
    "        similarity = num/den\n",
    "        \n",
    "    if similarity <= 0:\n",
    "        similarity = 0\n",
    "        \n",
    "    return similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将所有特征串联起来，构成RS_Train.csv\n",
    "#RS_Test.csv\n",
    "#为最后推荐系统做准备\n",
    "from __future__ import division\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "from numpy.random import random  \n",
    "from collections import defaultdict\n",
    "\n",
    "class RecommonderSystem:\n",
    "  def __init__(self):\n",
    "    # 读入数据做初始化\n",
    "    \n",
    "    #用户和活动新的索引\n",
    "    self.userIndex = pickle.load(open(dpath + \"PE_userIndex.pkl\", 'rb'))\n",
    "    self.eventIndex = pickle.load(open(dpath + \"PE_eventIndex.pkl\", 'rb'))\n",
    "    self.n_users = len(self.userIndex)\n",
    "    self.n_items = len(self.eventIndex)\n",
    "    #self.similarity = np.zeros((n_items, n_items), dtype=np.float)\n",
    "    #self.similarity[:,:] = -1\n",
    "    #用户-活动关系矩阵R\n",
    "    #在train_SVD会重新从文件中读取,二者要求的格式不同，来不及统一了:(\n",
    "    self.userEventScores = sio.mmread(dpath + \"PE_userEventScores\").todense()\n",
    "    \n",
    "    #倒排表\n",
    "    ##每个用户参加的事件\n",
    "    self.itemsForUser = pickle.load(open(dpath + \"PE_eventsForUser.pkl\", 'rb'))\n",
    "    ##事件参加的用户\n",
    "    self.usersForItem = pickle.load(open(dpath + \"PE_usersForEvent.pkl\", 'rb'))\n",
    "    \n",
    "    #基于模型的协同过滤参数初始化,训练\n",
    "    self.init_SVD()\n",
    "    self.train_SVD(trainfile = dpath + \"train.csv\")\n",
    "    \n",
    "    #根据用户属性计算出的用户之间的相似度\n",
    "    self.userSimMatrix = sio.mmread(dpath + \"US_userSimMatrix\").todense()\n",
    "    \n",
    "    #根据活动属性计算出的活动之间的相似度\n",
    "    self.eventPropSim = sio.mmread(dpath + \"EV_eventPropSim\").todense()\n",
    "    self.eventContSim = sio.mmread(dpath + \"EV_eventContSim\").todense()\n",
    "    \n",
    "    #每个用户的朋友的数目\n",
    "    self.numFriends = sio.mmread(dpath + \"UF_numFriends\")\n",
    "    #用户的每个朋友参加活动的分数对该用户的影响\n",
    "    self.userFriends = sio.mmread(dpath + \"UF_userFriends\").todense()\n",
    "    \n",
    "    #活动本身的热度\n",
    "    self.eventPopularity = sio.mmread(dpath + \"EA_eventPopularity\").todense()\n",
    "\n",
    "  def init_SVD(self, K=20):\n",
    "    #初始化模型参数（for 基于模型的协同过滤SVD_CF）\n",
    "    self.K = K  \n",
    "    \n",
    "    #init parameters\n",
    "    #bias\n",
    "    self.bi = np.zeros(self.n_items)  \n",
    "    self.bu = np.zeros(self.n_users)  \n",
    "    \n",
    "    #the small matrix\n",
    "    self.P = random((self.n_users,self.K)) / 10 *(np.sqrt(self.K))\n",
    "    self.Q = random((self.K, self.n_items)) / 10 * (np.sqrt(self.K))  \n",
    "                  \n",
    "          \n",
    "  def train_SVD(self,trainfile = dpath + 'train.csv', steps=100,gamma=0.04,Lambda=0.15):\n",
    "    #训练SVD模型（for 基于模型的协同过滤SVD_CF）\n",
    "    #gamma：为学习率\n",
    "    #Lambda：正则参数\n",
    "    \n",
    "    #偷懒了，为了和原来的代码的输入接口一样，直接从训练文件中去读取数据\n",
    "    print (\"SVD Train...\")\n",
    "    ftrain = open(trainfile, 'r')\n",
    "    ftrain.readline()\n",
    "    self.mu = 0.0\n",
    "    n_records = 0\n",
    "    uids = []  #每条记录的用户索引\n",
    "    i_ids = [] #每条记录的item索引\n",
    "    #用户-Item关系矩阵R（内容同userEventScores相同），临时变量，训练完了R不再需要\n",
    "    R = np.zeros((self.n_users, self.n_items))\n",
    "    \n",
    "    for line in ftrain:\n",
    "        cols = line.strip().split(\",\")\n",
    "        u = self.userIndex[cols[0]]  #用户\n",
    "        i = self.eventIndex[cols[1]] #活动\n",
    "        #用户id和物品id\n",
    "        uids.append(u)\n",
    "        i_ids.append(i)\n",
    "        \n",
    "        R[u,i] = int(cols[4])  #interested\n",
    "        self.mu += R[u,i]\n",
    "        n_records += 1\n",
    "    \n",
    "    ftrain.close()\n",
    "    self.mu /= n_records\n",
    "    \n",
    "    rmse_sum_old = 0.0\n",
    "    same_step = 0\n",
    "    for step in range(steps):\n",
    "        print('the ', step, '-th  step is running')\n",
    "        rmse_sum = 0.0\n",
    "        \n",
    "        kk = np.random.permutation(n_records) #将训练样本打散顺序\n",
    "        for j in range(n_records):\n",
    "            # 每次只训练一个样本\n",
    "            index = kk[j]\n",
    "            u = uids[index] #重新编排后的索引号\n",
    "            i = i_ids[index]\n",
    "            rat = R[u,i] # 获取真实的打分\n",
    "            \n",
    "            # 预测残差\n",
    "            eui = rat - self.pred_SVD(u, i) #真值减去预测值就是残差\n",
    "            # 残差平方和\n",
    "            rmse_sum += eui ** 2\n",
    "            \n",
    "            # 随机梯度下降更新\n",
    "            for k in range(self.K):\n",
    "                self.P[u,k] += gamma * eui * self.Q[k,i] - Lambda * self.P[u,k]\n",
    "                self.Q[k,i] += gamma * eui * self.P[u,k] - Lambda * self.Q[k,i]\n",
    "                \n",
    "            self.bu[u] += gamma * (eui - Lambda * self.bu[u])\n",
    "            self.bi[i] += gamma * (eui - Lambda * self.bi[i])\n",
    "            \n",
    "            # 学习率递减\n",
    "            gamma = gamma * 0.93\n",
    "        print(\"the rmse of this step on train data is \", np.sqrt(rmse_sum / n_records))\n",
    "\n",
    "\n",
    "    # 请补充完整SVD模型训练过程\n",
    "    print (\"SVD trained\")\n",
    "    \n",
    "  def pred_SVD(self, uid, i_id):\n",
    "    #根据当前参数，预测用户uid对Item（i_id）的打分        \n",
    "    ans=self.mu + self.bi[i_id] + self.bu[uid] + np.dot(self.P[uid,:],self.Q[:,i_id])  \n",
    "        \n",
    "    #将打分范围控制在0-1之间\n",
    "    if ans>1:  \n",
    "        return 1  \n",
    "    elif ans<0:  \n",
    "        return 0\n",
    "    return ans  \n",
    "\n",
    "  def sim_cal_UserCF(self, uid1, uid2):\n",
    "    #基于用户的协同过滤中的两个用户uid1和uid2之间的相似度（根据两个用户对item打分的相似度）\n",
    "    similarity = 0.0\n",
    "    #common events\n",
    "    common_events = {}  \n",
    "    for event in self.itemsForUser[uid1]:  #User1打过分的所有event\n",
    "        if event in self.itemsForUser[uid2]: #User1打过分的event中，判断是否也存在User2也打过分的相同event\n",
    "            #print (itemsForUser[i_id1])\n",
    "            #print (itemsForUser[i_id2])\n",
    "            common_events[event] = 1  #user为一个有效用用户\n",
    "        \n",
    "    #print (common_events)\n",
    "    n = len(common_events)   #有效用户数，有效用户为即对Item1打过分，也对Item2打过分\n",
    "        \n",
    "    #User1打过分的所有event的所有得分\n",
    "    s1 = np.array([self.userEventScores[uid1, e] for e in common_events])  \n",
    "        \n",
    "    #User2打过分的所有event的所有得分\n",
    "    s2 = np.array([self.userEventScores[uid2, e] for e in common_events]) \n",
    "    similarity = sim_cal(s1, s2)\n",
    "    return similarity  \n",
    "\n",
    "  def userCFReco(self, userId, eventId):\n",
    "    \"\"\"\n",
    "    根据User-based协同过滤，得到event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i\n",
    "      for every other user v that has a preference for i\n",
    "        compute similarity s between u and v\n",
    "        incorporate v's preference for i weighted by s into running aversge\n",
    "    return top items ranked by weighted average\n",
    "    \"\"\"\n",
    "    user_a = self.userIndex[userId]\n",
    "    event_b = self.eventIndex[eventId]\n",
    "    #找到用户a参加过的所有活动的评分\n",
    "    score_user_a = np.array([self.userEventScores[user_a, event] for event in self.itemsForUser[user_a]])\n",
    "    # 找出用户a所有参加过的活动，计算平均分\n",
    "    if len(score_user_a) == 0:\n",
    "        score_user_a_avg = 0\n",
    "    else:\n",
    "        score_user_a_avg = sum(score_user_a) / len(score_user_a)\n",
    "    \n",
    "    sim_num = 0.0 # 分子\n",
    "    sim_den = 0.0 #分母\n",
    "        \n",
    "    for user in self.usersForItem[event_b]: # 找到所有对这个物品打过分的用户\n",
    "        sim = self.sim_cal_UserCF(user_a, user) # 求出两个用户之间的相似度\n",
    "        if sim < 0:\n",
    "            continue\n",
    "        #用户user所有参加过的活动，并计算平均分\n",
    "        score_user = np.array([self.userEventScores[user, event] for event in self.itemsForUser[user]])\n",
    "        if len(score_user) == 0:\n",
    "            score_user_avg = 0\n",
    "        else:\n",
    "            score_user_avg = sum(score_user) / len(score_user)\n",
    "        \n",
    "        sim_den += sim # 分母累加\n",
    "        sim_num += sim * (self.userEventScores[user, event_b] - score_user_avg)# 找到当前user对事件j的打分,减去平均打分\n",
    "        \n",
    "    if sim_den == 0:\n",
    "        return 0\n",
    "    ans = score_user_a_avg + sim_num / sim_den #最后的结果再加上用户i的平均打分\n",
    "    # 将最后的结果控制在0-1之间\n",
    "    if ans > 1:\n",
    "        ans = 1\n",
    "    elif ans < 0:\n",
    "        ans = 0\n",
    "    return ans\n",
    "\n",
    "\n",
    "  def sim_cal_ItemCF(self, i_id1, i_id2):\n",
    "    #计算Item i_id1和i_id2之间的相似性\n",
    "    similarity = 0.0\n",
    "    #common events\n",
    "    common_users = {}  \n",
    "    for user in self.usersForItem[i_id1]:  #所有对item1打过分的user\n",
    "        if user in self.usersForItem[i_id2]: #获取同时对item1和item2打过分的users\n",
    "            #print (itemsForUser[i_id1])\n",
    "            #print (itemsForUser[i_id2])\n",
    "            common_users[user] = 1  #user为一个有效用用户\n",
    "        \n",
    "    #print (common_users)\n",
    "    n = len(common_users)   #有效用户数，有效用户为即对Item1打过分，也对Item2打过分\n",
    "        \n",
    "    #User1打过分的所有event的所有得分\n",
    "    s1 = np.array([self.userEventScores[i_id1, u] for u in common_users])  \n",
    "        \n",
    "    #User2打过分的所有event的所有得分\n",
    "    s2 = np.array([self.userEventScores[i_id2, u] for u in common_users]) \n",
    "    similarity = sim_cal(s1, s2)\n",
    "    return similarity  \n",
    "            \n",
    "  def eventCFReco(self, userId, eventId):    \n",
    "    \"\"\"\n",
    "    根据基于物品的协同过滤，得到Event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i \n",
    "        for every item j tht u has a preference for\n",
    "            compute similarity s between i and j\n",
    "            add u's preference for j weighted by s to a running average\n",
    "    return top items, ranked by weighted average\n",
    "    \"\"\"\n",
    "    i = self.userIndex[userId]\n",
    "    j = self.eventIndex[eventId]\n",
    "    \n",
    "    sim_num = 0.0 # 分子\n",
    "    sim_den = 0.0 #分母    \n",
    "    \n",
    "    for item in self.itemsForUser[i]: # 找到这个用户所有打过分的物品（这里是这个用户参加过的所有活动）\n",
    "        sim = self.sim_cal_UserCF(j, item) # 计算两个事件之间的相似度\n",
    "        if sim < 0:\n",
    "            continue\n",
    "                \n",
    "        sim_den += sim # 分母累加\n",
    "        sim_num += sim * self.userEventScores[i, item]# 根据公式计算加权得分\n",
    "        \n",
    "    if sim_den == 0:\n",
    "        return 0\n",
    "    ans = sim_num / sim_den #最后的结果\n",
    "    \n",
    "    # 将最后的结果控制在0-1之间\n",
    "    if ans > 1:\n",
    "        ans = 1\n",
    "    elif ans < 0:\n",
    "        ans = 0\n",
    "    return ans\n",
    "    \n",
    "  def svdCFReco(self, userId, eventId):\n",
    "    #基于模型的协同过滤, SVD++/LFM\n",
    "    u = self.userIndex[userId]\n",
    "    i = self.eventIndex[eventId]\n",
    "\n",
    "    return self.pred_SVD(u,i)\n",
    "\n",
    "  def userReco(self, userId, eventId):\n",
    "    \"\"\"\n",
    "    类似基于User-based协同过滤，只是用户之间的相似度由用户本身的属性得到，计算event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i\n",
    "      for every other user v that has a preference for i\n",
    "        compute similarity s between u and v\n",
    "        incorporate v's preference for i weighted by s into running aversge\n",
    "    return top items ranked by weighted average\n",
    "    \"\"\"\n",
    "    i = self.userIndex[userId]\n",
    "    j = self.eventIndex[eventId]\n",
    "\n",
    "    vs = self.userEventScores[:, j]\n",
    "    sims = self.userSimMatrix[i, :]\n",
    "\n",
    "    prod = sims * vs\n",
    "\n",
    "    try:\n",
    "      return prod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      return 0\n",
    "\n",
    "  def eventReco(self, userId, eventId):\n",
    "    \"\"\"\n",
    "    类似基于Item-based协同过滤，只是item之间的相似度由item本身的属性得到，计算Event的推荐度\n",
    "    基本的伪代码思路如下：\n",
    "    for item i \n",
    "      for every item j that u has a preference for\n",
    "        compute similarity s between i and j\n",
    "        add u's preference for j weighted by s to a running average\n",
    "    return top items, ranked by weighted average\n",
    "    \"\"\"\n",
    "    i = self.userIndex[userId]\n",
    "    j = self.eventIndex[eventId]\n",
    "    js = self.userEventScores[i, :]\n",
    "    psim = self.eventPropSim[:, j]\n",
    "    csim = self.eventContSim[:, j]\n",
    "    pprod = js * psim\n",
    "    cprod = js * csim\n",
    "    \n",
    "    pscore = 0\n",
    "    cscore = 0\n",
    "    try:\n",
    "      pscore = pprod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      pass\n",
    "    try:\n",
    "      cscore = cprod[0, 0] - self.userEventScores[i, j]\n",
    "    except IndexError:\n",
    "      pass\n",
    "    return pscore, cscore\n",
    "\n",
    "  def userPop(self, userId):\n",
    "    \"\"\"\n",
    "    基于用户的朋友个数来推断用户的社交程度\n",
    "    主要的考量是如果用户的朋友非常多，可能会更倾向于参加各种社交活动\n",
    "    \"\"\"\n",
    "    if userId in self.userIndex:\n",
    "      i = self.userIndex[userId]\n",
    "      try:\n",
    "        return self.numFriends[0, i]\n",
    "      except IndexError:\n",
    "        return 0\n",
    "    else:\n",
    "      return 0\n",
    "\n",
    "  def friendInfluence(self, userId):\n",
    "    \"\"\"\n",
    "    朋友对用户的影响\n",
    "    主要考虑用户所有的朋友中，有多少是非常喜欢参加各种社交活动/event的\n",
    "    用户的朋友圈如果都积极参与各种event，可能会对当前用户有一定的影响\n",
    "    \"\"\"\n",
    "    nusers = np.shape(self.userFriends)[1]\n",
    "    i = self.userIndex[userId]\n",
    "    return (self.userFriends[i, :].sum(axis=0) / nusers)[0,0]\n",
    "\n",
    "  def eventPop(self, eventId):\n",
    "    \"\"\"\n",
    "    本活动本身的热度\n",
    "    主要是通过参与的人数来界定的\n",
    "    \"\"\"\n",
    "    i = self.eventIndex[eventId]\n",
    "    return self.eventPopularity[i, 0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generateRSData(RS, train=True, header=True):\n",
    "    \"\"\"\n",
    "    把前面user-based协同过滤 和 item-based协同过滤，以及各种热度和影响度作为特征组合在一起\n",
    "    生成新的训练数据，用于分类器分类使用\n",
    "    \"\"\"\n",
    "    fn = \"train.csv\" if train else \"test.csv\"\n",
    "    fin = open(dpath + fn, 'rb')\n",
    "    fout = open(dpath + \"RS_\" + fn, 'wb')\n",
    "    \n",
    "    #忽略第一行（列名字）\n",
    "    fin.readline().strip().decode().split(\",\")\n",
    "    \n",
    "    # write output header\n",
    "    if header:\n",
    "      ocolnames = [\"invited\", \"userCF_reco\", \"evtCF_reco\",\"svdCF_reco\",\"user_reco\", \"evt_p_reco\",\n",
    "        \"evt_c_reco\", \"user_pop\", \"frnd_infl\", \"evt_pop\"]\n",
    "      if train:\n",
    "        ocolnames.append(\"interested\")\n",
    "        ocolnames.append(\"not_interested\")\n",
    "        #ocolnames.encode()\n",
    "        \n",
    "      s = \",\".join(ocolnames) + \"\\n\"\n",
    "      print (type(ocolnames))\n",
    "      print (type(s))\n",
    "      print (s)\n",
    "      fout.write(s.encode())\n",
    "    \n",
    "    ln = 0\n",
    "    for line in fin:\n",
    "      ln += 1\n",
    "      if ln%500 == 0:\n",
    "          print (\"%s:%d (userId, eventId)=(%s, %s)\" % (fn, ln, userId, eventId))\n",
    "          #break;\n",
    "      \n",
    "      cols = line.strip().decode().split(\",\")\n",
    "      userId = cols[0]\n",
    "      eventId = cols[1]\n",
    "      invited = cols[2]\n",
    "      \n",
    "      userCF_reco = RS.userCFReco(userId, eventId)\n",
    "      itemCF_reco = RS.eventCFReco(userId, eventId)\n",
    "      svdCF_reco = RS.svdCFReco(userId, eventId)\n",
    "        \n",
    "      user_reco = RS.userReco(userId, eventId)\n",
    "      evt_p_reco, evt_c_reco = RS.eventReco(userId, eventId)\n",
    "      user_pop = RS.userPop(userId)\n",
    "     \n",
    "      frnd_infl = RS.friendInfluence(userId)\n",
    "      evt_pop = RS.eventPop(eventId)\n",
    "      ocols = [invited, userCF_reco, itemCF_reco, svdCF_reco,user_reco, evt_p_reco,\n",
    "        evt_c_reco, user_pop, frnd_infl, evt_pop]\n",
    "      \n",
    "      if train:\n",
    "        ocols.append(cols[4]) # interested\n",
    "        ocols.append(cols[5]) # not_interested\n",
    "      content = \",\".join(map(lambda x: str(x), ocols)) + \"\\n\"\n",
    "      fout.write(content.encode())\n",
    "    \n",
    "    fin.close()\n",
    "    fout.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVD Train...\n",
      "the  0 -th  step is running\n",
      "the rmse of this step on train data is  0.668903679538\n",
      "the  1 -th  step is running\n",
      "the rmse of this step on train data is  0.474621992909\n",
      "the  2 -th  step is running\n",
      "the rmse of this step on train data is  0.448771491119\n",
      "the  3 -th  step is running\n",
      "the rmse of this step on train data is  0.444484336945\n",
      "the  4 -th  step is running\n",
      "the rmse of this step on train data is  0.443468563389\n",
      "the  5 -th  step is running\n",
      "the rmse of this step on train data is  0.443168018706\n",
      "the  6 -th  step is running\n",
      "the rmse of this step on train data is  0.443076730454\n",
      "the  7 -th  step is running\n",
      "the rmse of this step on train data is  0.443046422741\n",
      "the  8 -th  step is running\n",
      "the rmse of this step on train data is  0.443035720527\n",
      "the  9 -th  step is running\n",
      "the rmse of this step on train data is  0.443032608956\n",
      "the  10 -th  step is running\n",
      "the rmse of this step on train data is  0.44303149694\n",
      "the  11 -th  step is running\n",
      "the rmse of this step on train data is  0.443031073748\n",
      "the  12 -th  step is running\n",
      "the rmse of this step on train data is  0.443030938911\n",
      "the  13 -th  step is running\n",
      "the rmse of this step on train data is  0.443030902504\n",
      "the  14 -th  step is running\n",
      "the rmse of this step on train data is  0.443030883342\n",
      "the  15 -th  step is running\n",
      "the rmse of this step on train data is  0.443030875942\n",
      "the  16 -th  step is running\n",
      "the rmse of this step on train data is  0.443030872992\n",
      "the  17 -th  step is running\n",
      "the rmse of this step on train data is  0.443030872639\n",
      "the  18 -th  step is running\n",
      "the rmse of this step on train data is  0.443030872237\n",
      "the  19 -th  step is running\n",
      "the rmse of this step on train data is  0.443030872071\n",
      "the  20 -th  step is running\n",
      "the rmse of this step on train data is  0.44303087205\n",
      "the  21 -th  step is running\n",
      "the rmse of this step on train data is  0.443030872011\n",
      "the  22 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871999\n",
      "the  23 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871995\n",
      "the  24 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871993\n",
      "the  25 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  26 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  27 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  28 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  29 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  30 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  31 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  32 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  33 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  34 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  35 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  36 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  37 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  38 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  39 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  40 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  41 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  42 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  43 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  44 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  45 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  46 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  47 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  48 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  49 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  50 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  51 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  52 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  53 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  54 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  55 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  56 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  57 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  58 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  59 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  60 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  61 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  62 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  63 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  64 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  65 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  66 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  67 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  68 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  69 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  70 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  71 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  72 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  73 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  74 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  75 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  76 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  77 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  78 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  79 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  80 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  81 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  82 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  83 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  84 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  85 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  86 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  87 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  88 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  89 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  90 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  91 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  92 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  93 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  94 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  95 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  96 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  97 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "the  98 -th  step is running\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the rmse of this step on train data is  0.443030871992\n",
      "the  99 -th  step is running\n",
      "the rmse of this step on train data is  0.443030871992\n",
      "SVD trained\n",
      "生成训练数据...\n",
      "\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "invited,userCF_reco,evtCF_reco,svdCF_reco,user_reco,evt_p_reco,evt_c_reco,user_pop,frnd_infl,evt_pop,interested,not_interested\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\python3\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train.csv:500 (userId, eventId)=(123290209, 1887085024)\n",
      "train.csv:1000 (userId, eventId)=(272886293, 199858305)\n",
      "train.csv:1500 (userId, eventId)=(395305791, 1582270949)\n",
      "train.csv:2000 (userId, eventId)=(527523423, 3272728211)\n",
      "train.csv:2500 (userId, eventId)=(651258472, 792632006)\n",
      "train.csv:3000 (userId, eventId)=(811791433, 524756826)\n",
      "train.csv:3500 (userId, eventId)=(985547042, 1269035551)\n",
      "train.csv:4000 (userId, eventId)=(1107615001, 173949238)\n",
      "train.csv:4500 (userId, eventId)=(1236336671, 3849306291)\n",
      "train.csv:5000 (userId, eventId)=(1414301782, 2652356640)\n",
      "train.csv:5500 (userId, eventId)=(1595465532, 955398943)\n",
      "train.csv:6000 (userId, eventId)=(1747091728, 2131379889)\n",
      "train.csv:6500 (userId, eventId)=(1914182220, 955398943)\n",
      "train.csv:7000 (userId, eventId)=(2071842684, 1076364848)\n",
      "train.csv:7500 (userId, eventId)=(2217853337, 3051438735)\n",
      "train.csv:8000 (userId, eventId)=(2338481531, 2525447278)\n",
      "train.csv:8500 (userId, eventId)=(2489551967, 520657921)\n",
      "train.csv:9000 (userId, eventId)=(2650493630, 87962584)\n",
      "train.csv:9500 (userId, eventId)=(2791418962, 4223848259)\n",
      "train.csv:10000 (userId, eventId)=(2903662804, 2791462807)\n",
      "train.csv:10500 (userId, eventId)=(3036141956, 3929507420)\n",
      "train.csv:11000 (userId, eventId)=(3176074542, 3459485614)\n",
      "train.csv:11500 (userId, eventId)=(3285425249, 2271782630)\n",
      "train.csv:12000 (userId, eventId)=(3410667855, 1063772489)\n",
      "train.csv:12500 (userId, eventId)=(3531604778, 2584839423)\n",
      "train.csv:13000 (userId, eventId)=(3686871863, 53495098)\n",
      "train.csv:13500 (userId, eventId)=(3833637800, 2415873572)\n",
      "train.csv:14000 (userId, eventId)=(3944021305, 2096772901)\n",
      "train.csv:14500 (userId, eventId)=(4075466480, 3567240505)\n",
      "train.csv:15000 (userId, eventId)=(4197193550, 1628057176)\n",
      "生成预测数据...\n",
      "\n",
      "<class 'list'>\n",
      "<class 'str'>\n",
      "invited,userCF_reco,evtCF_reco,svdCF_reco,user_reco,evt_p_reco,evt_c_reco,user_pop,frnd_infl,evt_pop\n",
      "\n",
      "test.csv:500 (userId, eventId)=(182290053, 2529072432)\n",
      "test.csv:1000 (userId, eventId)=(433510318, 4244463632)\n",
      "test.csv:1500 (userId, eventId)=(632808865, 2845303452)\n",
      "test.csv:2000 (userId, eventId)=(813611885, 2036538169)\n",
      "test.csv:2500 (userId, eventId)=(1010701404, 303459881)\n",
      "test.csv:3000 (userId, eventId)=(1210932037, 2529072432)\n",
      "test.csv:3500 (userId, eventId)=(1452921099, 2705317682)\n",
      "test.csv:4000 (userId, eventId)=(1623287180, 1626678328)\n",
      "test.csv:4500 (userId, eventId)=(1855201342, 2603032829)\n",
      "test.csv:5000 (userId, eventId)=(2083900381, 2529072432)\n",
      "test.csv:5500 (userId, eventId)=(2318415276, 2509151803)\n",
      "test.csv:6000 (userId, eventId)=(2528161539, 4025975316)\n",
      "test.csv:6500 (userId, eventId)=(2749110768, 4244406355)\n",
      "test.csv:7000 (userId, eventId)=(2927772127, 1532377761)\n",
      "test.csv:7500 (userId, eventId)=(3199685636, 1776393554)\n",
      "test.csv:8000 (userId, eventId)=(3393388475, 680270887)\n",
      "test.csv:8500 (userId, eventId)=(3601169721, 154434302)\n",
      "test.csv:9000 (userId, eventId)=(3828963415, 3067222491)\n",
      "test.csv:9500 (userId, eventId)=(4018723397, 2522610844)\n",
      "test.csv:10000 (userId, eventId)=(4180064266, 2658555390)\n"
     ]
    }
   ],
   "source": [
    "RS = RecommonderSystem()\n",
    "print (\"生成训练数据...\\n\")\n",
    "generateRSData(RS,train=True,  header=True)\n",
    "\n",
    "print (\"生成预测数据...\\n\")\n",
    "generateRSData(RS, train=False, header=True)"
   ]
  }
 ],
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